Module ojalgo

Package org.ojalgo.data

Class DataProcessors

java.lang.Object

org.ojalgo.data.DataProcessors

public class DataProcessors
extends java.lang.Object

Various data processors that could be useful when doing data science or similar. With ojAlgo it is highly advantageous to store data in columns (rather than rows). All the Transformation2D instances in this class assume columns represent variables, and rows samples.

Field Summary

Fields

Modifier and Type	Field	Description
static Transformation2D<java.lang.Double>	CENTER	Variables (columns) centered so that their average will be 0.0
static Transformation2D<java.lang.Double>	CENTER_AND_SCALE	Variables (columns) will be transformed to be [-1.0,1.0].
static Transformation2D<java.lang.Double>	SCALE	Variables (columns) scaled to be within [-1.0,1.0] (divide by largest magnitude regardless of sign).
static Transformation2D<java.lang.Double>	STANDARD_SCORE	Will normalise each variable (columns) - replace each value with its standard score.

Constructor Summary

Constructors

Constructor	Description
DataProcessors()

Method Summary

Modifier and Type	Method	Description
static <M extends Mutate2D> M	correlations(Factory2D<M> factory, Access1D<?>... data)	Calculate the correlation matrix from a set of variables' samples.
static <M extends Mutate2D> M	covariances(Factory2D<M> factory, double[]... data)
static <D extends Access2D<?> & Access2D.Sliceable<?>,M extends Mutate2D> M	covariances(Factory2D<M> factory, D data)	Variables in columns and matching samples in rows.
static <M extends PhysicalStore<java.lang.Double>> M	covariances(Factory2D<M> factory, SingularValue<java.lang.Double> svd)
static <M extends PhysicalStore<java.lang.Double>> M	covariances(Factory2D<M> factory, SingularValue<java.lang.Double> svd, double threshold)
static <M extends PhysicalStore<java.lang.Double>> M	covariances(Factory2D<M> factory, SingularValue<java.lang.Double> svd, int complexity)
static <M extends Mutate2D> M	covariances(Factory2D<M> factory, Access1D<?>... data)	Calculate the covariance matrix from a set of variables' samples.
static Transformation2D<java.lang.Double>	newColumnsTransformer(java.util.function.Function<SampleSet,UnaryFunction<java.lang.Double>> definition)	Creates a Transformation2D that will apply a UnaryFunction to each column.
static Transformation2D<java.lang.Double>	newRowsTransformer(java.util.function.Function<SampleSet,UnaryFunction<java.lang.Double>> definition)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

CENTER

public static final Transformation2D<java.lang.Double> CENTER

Variables (columns) centered so that their average will be 0.0

CENTER_AND_SCALE

public static final Transformation2D<java.lang.Double> CENTER_AND_SCALE

Variables (columns) will be transformed to be [-1.0,1.0]. The minimum value will be transformed to -1.0 and the maximum to +1.0. The midrange will be transformed to 0.0.

SCALE

public static final Transformation2D<java.lang.Double> SCALE

Variables (columns) scaled to be within [-1.0,1.0] (divide by largest magnitude regardless of sign). If all values are positive the range will within [0.0,1.0]. If all are negative the range will be within [-1.0,0.0]

STANDARD_SCORE

public static final Transformation2D<java.lang.Double> STANDARD_SCORE

Will normalise each variable (columns) - replace each value with its standard score.

Constructor Detail

DataProcessors

public DataProcessors()

Method Detail

correlations

public static <M extends Mutate2D> M correlations(Factory2D<M> factory,
                                                  Access1D<?>... data)

Calculate the correlation matrix from a set of variables' samples. Each Access1D instance represents one variable, and contains an ordered sequence of samples.

covariances

public static <M extends Mutate2D> M covariances(Factory2D<M> factory,
                                                 Access1D<?>... data)

Calculate the covariance matrix from a set of variables' samples. Each Access1D instance represents one variable, and contains an ordered sequence of samples.

covariances

public static <D extends Access2D<?> & Access2D.Sliceable<?>,M extends Mutate2D> M covariances(Factory2D<M> factory,
                                                                                                     D data)

Variables in columns and matching samples in rows.

See Also:

covariances(Factory2D, Access1D...)

covariances

public static <M extends Mutate2D> M covariances(Factory2D<M> factory,
                                                 double[]... data)

See Also:

covariances(Factory2D, Access1D...)

covariances

public static <M extends PhysicalStore<java.lang.Double>> M covariances(Factory2D<M> factory,
                                                                        SingularValue<java.lang.Double> svd)

See Also:

covariances(Factory2D, SingularValue, int)

covariances

public static <M extends PhysicalStore<java.lang.Double>> M covariances(Factory2D<M> factory,
                                                                        SingularValue<java.lang.Double> svd,
                                                                        double threshold)

See Also:

covariances(Factory2D, SingularValue, int)

covariances

public static <M extends PhysicalStore<java.lang.Double>> M covariances(Factory2D<M> factory,
                                                                        SingularValue<java.lang.Double> svd,
                                                                        int complexity)

Parameters:

factory - A factory that will produce the returned covariance matrix

svd - A pre-decomposed SVD instance. The original matrix is assumed to have centered data in its columns

complexity - The maximum number of singular values that should be considered

newColumnsTransformer

public static Transformation2D<java.lang.Double> newColumnsTransformer(java.util.function.Function<SampleSet,UnaryFunction<java.lang.Double>> definition)

Creates a Transformation2D that will apply a UnaryFunction to each column. That unary function will be created by the provided Function using a SampleSet (of that column) as input.

The constants CENTER, SCALE, CENTER_AND_SCALE and STANDARD_SCORE are predefined Transformation2D instances created by calling this method.

Parameters:

definition - A Function that will create a UnaryFunction from a SampleSet to be applied to each column

Returns:

A Transformation2D that will apply a UnaryFunction to each column

newRowsTransformer

public static Transformation2D<java.lang.Double> newRowsTransformer(java.util.function.Function<SampleSet,UnaryFunction<java.lang.Double>> definition)